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   "source": [
    "## Exercícios - JADER GOMES\n",
    "\n",
    "1 - Aplique os algoritmos K-means [1] e AgglomerativeClustering [2] em qualquer dataset que você desejar (recomendação: iris). Compare os resultados utilizando métricas de avaliação de clusteres (completeness e homogeneity, por exemplo) [3].\n",
    "\n",
    "* [1] http://scikit-learn.org/stable/modules/clustering.html#k-means\n",
    "\n",
    "* [2] http://scikit-learn.org/0.17/modules/clustering.html#hierarchical-clustering\n",
    "\n",
    "* [3] http://scikit-learn.org/stable/modules/clustering.html#clustering-evaluation\n",
    "\n",
    "2 - Qual o valor de K (número de clusteres) você escolheu para a questão anterior? Desenvolva o Método do Cotovelo (não utilizar lib!) e descubra o K mais adequado. Após descobrir, aplique novamente o K-means com o K adequado. \n",
    "\n",
    "* Ajuda: atributos do [k-means](http://scikit-learn.org/0.17/modules/generated/sklearn.cluster.KMeans.html#sklearn.cluster.KMeans)\n",
    "\n",
    "3 - Após a questão 2, você aplicou o algoritmo com K apropriado. Refaça o cálculo das métricas de acordo com os resultados de clusters obtidos com a questão anterior e verifique se o resultado melhorou.\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Ex. 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn import metrics\n",
    "from sklearn import datasets\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import normalize\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = datasets.load_iris()\n",
    "df = pd.DataFrame(iris.data)\n",
    "y = iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "       0    1    2    3\n",
      "0    5.1  3.5  1.4  0.2\n",
      "1    4.9  3.0  1.4  0.2\n",
      "2    4.7  3.2  1.3  0.2\n",
      "3    4.6  3.1  1.5  0.2\n",
      "4    5.0  3.6  1.4  0.2\n",
      "5    5.4  3.9  1.7  0.4\n",
      "6    4.6  3.4  1.4  0.3\n",
      "7    5.0  3.4  1.5  0.2\n",
      "8    4.4  2.9  1.4  0.2\n",
      "9    4.9  3.1  1.5  0.1\n",
      "10   5.4  3.7  1.5  0.2\n",
      "11   4.8  3.4  1.6  0.2\n",
      "12   4.8  3.0  1.4  0.1\n",
      "13   4.3  3.0  1.1  0.1\n",
      "14   5.8  4.0  1.2  0.2\n",
      "15   5.7  4.4  1.5  0.4\n",
      "16   5.4  3.9  1.3  0.4\n",
      "17   5.1  3.5  1.4  0.3\n",
      "18   5.7  3.8  1.7  0.3\n",
      "19   5.1  3.8  1.5  0.3\n",
      "20   5.4  3.4  1.7  0.2\n",
      "21   5.1  3.7  1.5  0.4\n",
      "22   4.6  3.6  1.0  0.2\n",
      "23   5.1  3.3  1.7  0.5\n",
      "24   4.8  3.4  1.9  0.2\n",
      "25   5.0  3.0  1.6  0.2\n",
      "26   5.0  3.4  1.6  0.4\n",
      "27   5.2  3.5  1.5  0.2\n",
      "28   5.2  3.4  1.4  0.2\n",
      "29   4.7  3.2  1.6  0.2\n",
      "..   ...  ...  ...  ...\n",
      "120  6.9  3.2  5.7  2.3\n",
      "121  5.6  2.8  4.9  2.0\n",
      "122  7.7  2.8  6.7  2.0\n",
      "123  6.3  2.7  4.9  1.8\n",
      "124  6.7  3.3  5.7  2.1\n",
      "125  7.2  3.2  6.0  1.8\n",
      "126  6.2  2.8  4.8  1.8\n",
      "127  6.1  3.0  4.9  1.8\n",
      "128  6.4  2.8  5.6  2.1\n",
      "129  7.2  3.0  5.8  1.6\n",
      "130  7.4  2.8  6.1  1.9\n",
      "131  7.9  3.8  6.4  2.0\n",
      "132  6.4  2.8  5.6  2.2\n",
      "133  6.3  2.8  5.1  1.5\n",
      "134  6.1  2.6  5.6  1.4\n",
      "135  7.7  3.0  6.1  2.3\n",
      "136  6.3  3.4  5.6  2.4\n",
      "137  6.4  3.1  5.5  1.8\n",
      "138  6.0  3.0  4.8  1.8\n",
      "139  6.9  3.1  5.4  2.1\n",
      "140  6.7  3.1  5.6  2.4\n",
      "141  6.9  3.1  5.1  2.3\n",
      "142  5.8  2.7  5.1  1.9\n",
      "143  6.8  3.2  5.9  2.3\n",
      "144  6.7  3.3  5.7  2.5\n",
      "145  6.7  3.0  5.2  2.3\n",
      "146  6.3  2.5  5.0  1.9\n",
      "147  6.5  3.0  5.2  2.0\n",
      "148  6.2  3.4  5.4  2.3\n",
      "149  5.9  3.0  5.1  1.8\n",
      "\n",
      "[150 rows x 4 columns]\n"
     ]
    }
   ],
   "source": [
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
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       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
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       "     0    1    2    3\n",
       "0  5.1  3.5  1.4  0.2\n",
       "1  4.9  3.0  1.4  0.2\n",
       "2  4.7  3.2  1.3  0.2\n",
       "3  4.6  3.1  1.5  0.2\n",
       "4  5.0  3.6  1.4  0.2"
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   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Normalizando\n",
    "normal = normalize(df)/2\n",
    "df = pd.DataFrame(normal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
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     "name": "stdout",
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      "            0         1         2         3\n",
      "0    0.401886  0.275804  0.110322  0.015760\n",
      "1    0.414066  0.253510  0.118305  0.016901\n",
      "2    0.402667  0.274156  0.111376  0.017135\n",
      "3    0.400015  0.269575  0.130440  0.017392\n",
      "4    0.395482  0.284747  0.110735  0.015819\n",
      "5    0.392087  0.283174  0.123435  0.029044\n",
      "6    0.390055  0.288301  0.118712  0.025438\n",
      "7    0.401092  0.272743  0.120328  0.016044\n",
      "8    0.403212  0.265753  0.128295  0.018328\n",
      "9    0.409016  0.258765  0.125209  0.008347\n",
      "10   0.401868  0.275354  0.111630  0.014884\n",
      "11   0.393496  0.278726  0.131165  0.016396\n",
      "12   0.411536  0.257210  0.120031  0.008574\n",
      "13   0.401256  0.279946  0.102647  0.009332\n",
      "14   0.405604  0.279727  0.083918  0.013986\n",
      "15   0.386906  0.298664  0.101817  0.027151\n",
      "16   0.397145  0.286827  0.095609  0.029418\n",
      "17   0.401637  0.275633  0.110253  0.023626\n",
      "18   0.403414  0.268943  0.120316  0.021232\n",
      "19   0.389824  0.290457  0.114654  0.022931\n",
      "20   0.408669  0.257310  0.128655  0.015136\n",
      "21   0.392959  0.285088  0.115576  0.030820\n",
      "22   0.387885  0.303562  0.084323  0.016865\n",
      "23   0.402989  0.260758  0.134330  0.039509\n",
      "24   0.388057  0.274874  0.153606  0.016169\n",
      "25   0.413237  0.247942  0.132236  0.016529\n",
      "26   0.398891  0.271246  0.127645  0.031911\n",
      "27   0.403210  0.271391  0.116311  0.015508\n",
      "28   0.408047  0.266800  0.109859  0.015694\n",
      "29   0.397620  0.270720  0.135360  0.016920\n",
      "..        ...       ...       ...       ...\n",
      "120  0.352795  0.163615  0.291439  0.117598\n",
      "121  0.341540  0.170770  0.298847  0.121978\n",
      "122  0.357433  0.129976  0.311013  0.092840\n",
      "123  0.365612  0.156691  0.284365  0.104461\n",
      "124  0.347978  0.171392  0.296041  0.109068\n",
      "125  0.357647  0.158954  0.298039  0.089412\n",
      "126  0.363926  0.164354  0.281749  0.105656\n",
      "127  0.355856  0.175011  0.285852  0.105007\n",
      "128  0.347970  0.152237  0.304474  0.114178\n",
      "129  0.365449  0.152271  0.294390  0.081211\n",
      "130  0.363831  0.137666  0.299915  0.093416\n",
      "131  0.357895  0.172152  0.289940  0.090606\n",
      "132  0.347089  0.151851  0.303703  0.119312\n",
      "133  0.361830  0.160813  0.292910  0.086150\n",
      "134  0.346927  0.147871  0.318490  0.079623\n",
      "135  0.365772  0.142509  0.289767  0.109257\n",
      "136  0.335087  0.180841  0.297855  0.127652\n",
      "137  0.349024  0.169058  0.299942  0.098163\n",
      "138  0.355335  0.177667  0.284268  0.106600\n",
      "139  0.362076  0.162672  0.283364  0.110197\n",
      "140  0.349985  0.161933  0.292525  0.125368\n",
      "141  0.366689  0.164745  0.271031  0.122230\n",
      "142  0.345263  0.160726  0.303593  0.113103\n",
      "143  0.345968  0.162808  0.300178  0.117018\n",
      "144  0.344574  0.169716  0.293145  0.128573\n",
      "145  0.360779  0.161543  0.280007  0.123849\n",
      "146  0.364827  0.144773  0.289545  0.110027\n",
      "147  0.358269  0.165355  0.286616  0.110237\n",
      "148  0.337335  0.184990  0.293808  0.125141\n",
      "149  0.345130  0.175490  0.298332  0.105294\n",
      "\n",
      "[150 rows x 4 columns]\n"
     ]
    }
   ],
   "source": [
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "      <th>0</th>\n",
       "      <td>0.401886</td>\n",
       "      <td>0.275804</td>\n",
       "      <td>0.110322</td>\n",
       "      <td>0.015760</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.414066</td>\n",
       "      <td>0.253510</td>\n",
       "      <td>0.118305</td>\n",
       "      <td>0.016901</td>\n",
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       "      <th>2</th>\n",
       "      <td>0.402667</td>\n",
       "      <td>0.274156</td>\n",
       "      <td>0.111376</td>\n",
       "      <td>0.017135</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.400015</td>\n",
       "      <td>0.269575</td>\n",
       "      <td>0.130440</td>\n",
       "      <td>0.017392</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.395482</td>\n",
       "      <td>0.284747</td>\n",
       "      <td>0.110735</td>\n",
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       "          0         1         2         3\n",
       "0  0.401886  0.275804  0.110322  0.015760\n",
       "1  0.414066  0.253510  0.118305  0.016901\n",
       "2  0.402667  0.274156  0.111376  0.017135\n",
       "3  0.400015  0.269575  0.130440  0.017392\n",
       "4  0.395482  0.284747  0.110735  0.015819"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_pca = PCA(n_components=2).fit_transform(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc55b3530b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots()\n",
    "ax.scatter(df_pca[:,0], df_pca[:,1], c=y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# clusterização usando KMeans \n",
    "km = KMeans(n_clusters=3)\n",
    "km.fit(df_pca)\n",
    "clusters = km.predict(df_pca)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc5534919b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots()\n",
    "ax.scatter(df_pca[:,0], df_pca[:,1], c=clusters)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "homogeneity = 0.8983263672602775\n",
      "completeness = 0.9010648908640206\n"
     ]
    }
   ],
   "source": [
    "#avaliando os clusteres\n",
    "score_homo = metrics.homogeneity_score(y,clusters)\n",
    "score_comp = metrics.completeness_score(y,clusters) \n",
    "\n",
    "print('homogeneity = {0}'.format(score_homo))\n",
    "print('completeness = {0}'.format(score_comp))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ex. 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "clusters_result = []\n",
    "for i in range(9):\n",
    "    km = KMeans(n_clusters=i+1)\n",
    "    km.fit(df_pca)   \n",
    "    clusters_result.append(km.inertia_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc553455cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(np.arange(1,10),clusters_result,'ro-')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# KMeans clustering\n",
    "km = KMeans(n_clusters=2)\n",
    "km.fit(df_pca)\n",
    "clusters = km.predict(df_pca)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc5532cf160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots()\n",
    "ax.scatter(df_pca[:,0], df_pca[:,1], c=clusters)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ex. 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "homogeneity = 0.5793801642856945\n",
      "completeness = 0.9999999999999997\n"
     ]
    }
   ],
   "source": [
    "#avaliando os clusteres\n",
    "score_homo = metrics.homogeneity_score(y,clusters)\n",
    "score_comp = metrics.completeness_score(y,clusters) \n",
    "\n",
    "print('homogeneity = {0}'.format(score_homo))\n",
    "print('completeness = {0}'.format(score_comp))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
